SoroushVahidi/scidocs huggingface dataset:
Creating a Labeled Dataset for Medical Misinformation in Health Forums
(2017)

The dissemination of medical misinformation online presents a challenge to human health. Machine learning techniques provide a unique opportunity for decreasing the cognitive load associated with deciding upon whether any given user comment is likely to contain misinformation, but a paucity of labeled data of medical misinformation makes supervised approaches a challenge. In order to ameliorate this condition, we present a new labeled dataset of misinformative and non-misinformative comments developed over posted questions and comments on a health discussion forum. This required extraction of candidate misinformative entries from the corpus using information retrieval techniques, development of a codex and labeling strategy for the dataset, and the creation of features for use in machine learning tasks. By identifying the nine most descriptive features with regard to classification as misinformative or non-misinformative through the use of Recursive Feature Elimination, we achieved a classification accuracy of 90.1%, where the dataset is comprised 85.8% of non-misinformative comments. In our opinion, this dataset and analysis will aid the machine learning community in the development of an online misinformation classification system over user-generated content such as medical forum posts.
Evaluating visual analytics with eye tracking
(2014)

The application of eye tracking for the evaluation of humans' viewing behavior is a common approach in psychological research. So far, the use of this technique for the evaluation of visual analytics and visualization is less prominent. We investigate recent scientific publications from the main visualization and visual analytics conferences and journals that include an evaluation by eye tracking. Furthermore, we provide an overview of evaluation goals that can be achieved by eye tracking and state-of-the-art analysis techniques for eye tracking data. Ideally, visual analytics leads to a mixed-initiative cognitive system where the mechanism of distribution is the interaction of the user with visualization environments. Therefore, we also include a discussion of cognitive approaches and models to include the user in the evaluation process. Based on our review of the current use of eye tracking evaluation in our field and the cognitive theory, we propose directions of future research on evaluation methodology, leading to the grand challenge of developing an evaluation approach to the mixed-initiative cognitive system of visual analytics.
Game theoretic approach on Real-time decision making for IoT-based traffic light control
(2017)

1Department of Computer Engineering, Chung-Ang University, Seoul, Korea 2Department of Computer Science, Universidad Autônoma de Madrid, Spain Correspondence Jai E. Jung, Department of Computer Engineering, Chung-Ang University, Korea. Email: j3ung@cau.ac.kr David Camacho, Department of Computer Science, Universidad Autonoma de Madrid, Spain. Email: david.camacho@uam.es Funding information Chung-Ang University Summary Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real-time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real-time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent-based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.
Pulsed voltage converter with bipolar output voltages up to 10 kV for Dielectric Barrier Discharge
(2011)

For pulsed power applications special pulsed voltage converters are needed. This paper presents an approach for a pulsed power converter, which generates bipolar output voltages up to 10 kV with extremely fast voltage slopes and high repetition rates. The topology is based on a H-bridge with a 10 kV dc-link. The output voltage and current of the pulsed voltage converter are adapted for the operation with Dielectric Barrier Discharge. To avoid the use of spark gaps due to their limited lifetime and thus to the lifetime of the converter, series stacked MOSFETs are used to realize a switch with a high blocking voltage. A balancing network for the series stacked MOSFETs is introduced as well as an adequate gate drive circuit. A matching method for the capacitive load is described, to achieve a maximum voltage slope at this capacitive load. To validate the theoretical considerations a prototype and measurements are presented.
Odor Recognition in Multiple E-Nose Systems With Cross-Domain Discriminative Subspace Learning
(2017)

In this paper, we propose an odor recognition framework for multiple electronic noses (E-noses), machine olfaction odor perception systems. Straight to the point, the proposed transferring odor recognition model is called cross-domain discriminative subspace learning (CDSL). General odor recognition problems with E-nose are single domain oriented, that is, recognition algorithms are often modeled and tested on the same one domain data set (i.e., from only one E-nose system). Different from that, we focus on a more realistic scenario: the recognition model is trained on a prepared source domain data set from a master E-nose system <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>, but tested on another target domain data set from a slave system <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> with the same type of the master system <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>. The internal device parameter variance between master and slave systems often results in data distribution discrepancy between source domain and target domain, such that single-domain-based odor recognition model may not be adapted to another domain. Therefore, we propose domain-adaptation-based odor recognition for addressing the realistic recognition scenario across systems. Specifically, the proposed CDSL method consists of three merits: 1) an intraclass scatter minimization- and an interclass scatter maximization-based discriminative subspace learning is solved on source domain; 2) a data fidelity and preservation constraint of the subspace is imposed on target domain without distortion; and 3) a minipatch feature weighted domain distance is minimized for closely connecting the source and target domains. Experiments and comparisons on odor recognition tasks in multiple E-noses demonstrate the efficiency of the proposed method.
Real Time Drowsy Driver Identification Using Eye Blink Detection
(2015)

HUMAN COMPUTER INTERFACE (HCI) systems are designed for use in assisting people in various aspects. Driving support systems such as navigating systems are getting commoner by the day. The capability of driving support systems to detect the level of driver’s alertness is very important in ensuring road safety. By observation of blink pattern and eye movements, driver fatigue can be detected early enough to prevent collisions caused by drowsiness. The analysis of face image are widely used in security systems, face recognition, criminal focusing etc. In this specific study a non recursive system have been designed to detect shutting of eyes of the person driving an automobile. Real time detection of driver’s eyes is processed using image processing in MATLAB to detect whether the eye remains closed more than the fixed duration thus indicating condition of fatigue and raise an alarm which could prevent a collision. The driving support systems have been found lacking in detecting the influence of drug or alcohol causing great degree of risks to the commuters. This study has found that eye blink patterns are starkly different for persons under the influence of drugs and can be easily detected by the system designed by us.
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
(2018)

Recurrent neural network (RNN) models are widely used for processing sequential data governed by a latent tree structure. Previous work shows that RNN models (especially Long Short-Term Memory (LSTM) based models) could learn to exploit the underlying tree structure. However, its performance consistently lags behind that of tree-based models. This work proposes a new inductive bias Ordered Neurons, which enforces an order of updating frequencies between hidden state neurons. We show that the ordered neurons could explicitly integrate the latent tree structure into recurrent models. To this end, we propose a new RNN unit: ON-LSTM, which achieve good performances on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference1.
The Pendubot: a mechatronic system for control research and education
(1995)

1 In this paper we describe the Pendubot, a mechatronic device for use in control engineering education and for research in nonlinear control and robotics. This device is a two{link planar robot with an actuator at the shoulder but no actuator at the elbow. With this system , a number of fundamental concepts in nonlinear dynamics and control theory may be illustrated. The pendubot complements previous mechatronic systems, such as the Acrobot 3] and the inverted pendulum of Furuta 4].
Extensional Versus Intuitive Reasoning : The Conjunction Fallacy in Probability Judgment
(1983)

Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and .P(B), because the extension (or the possibility set) of the conjunction is included in the extension of its constituents. Judgments under uncertainty, however, are often mediated by intuitive heuristics that are not bound by the conjunction rule. A conjunction can be more representative than one of its constituents, and instances of a specific category can be easier to imagine or to retrieve than instances of a more inclusive category. The representativeness and availability heuristics therefore can make a conjunction appear more probable than one of its constituents. This phenomenon is demonstrated in a variety of contexts including estimation of word frequency, personality judgment, medical prognosis, decision under risk, suspicion of criminal acts, and political forecasting. Systematic violations of the conjunction rule are observed in judgments of lay people and of experts in both between-subjects and within-subjects comparisons. Alternative interpretations of the conjunction fallacy are discussed and attempts to combat it are explored.
An Achilles Heel in Signature-Based IDS : Squealing False Positives in SNORT
(2001)

We report a vulnerability to network signature-based IDS which we have tested using Snort and we call “Squealing”. This vulnerability has significant implications since it can easily be generalized to any IDS. The vulnerability of signature-based IDS to high false positive rates has been welldocumented but we go further to show (at a high level) how packets can be crafted to match attack signatures such that a alarms on a target IDS can be conditioned or disabled and then exploited. This is the first academic treatment of this vulnerability that has already been reported to the CERT Coordination Center and the National Infrastructure Protection Center. Independently, other tools based on “squealing” are poised to appear that, while validating our ideas, also gives cause for concern. keywords: squealing, false positive, intrusion detection, IDS, signature-based, misuse behavior, network intrusion detection, snort
Snort: Lightweight Intrusion Detection for Networks
(1999)

Network intrusion detection systems (NIDS) are an important part of any network security architecture. They provide a layer of defense which monitors network traffic for predefined suspicious activity or patterns, and alert system administrators when potential hostile traffic is detected. Commercial NIDS have many differences, but Information Systems departments must face the commonalities that they share such as significant system footprint, complex deployment and high monetary cost. Snort was designed to address these issues.
Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation
(2000)

A intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100’s of users on 1000’s of hosts. More than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data. Six research groups participated in a blind evaluation and results were analyzed for probe, denialof-service (DoS), remote-to-local (R2L), and user to root (U2R) attacks. The best systems detected old attacks included in the training data, at moderate detection rates ranging from 63% to 93% at a false alarm rate of 10 false alarms per day. Detection rates were much worse for new and novel R2L and DoS attacks included only in the test data. The best systems failed to detect roughly half these new attacks which included damaging access to root-level privileges by remote users. These results suggest that further research should focus on developing techniques to find new attacks instead of extending existing rule-based
Data Preparation for Mining World Wide Web Browsing Patterns
(1999)

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system [15].
Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics
(2016)

This paper studies differential privacy for stochastic gradient descent (SGD) in an in-RDBMS system. While significant progress has been made separately on in-RDBMS SGD and private SGD, none of the major in-RDBMS machine learning frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner. In contrast to the white-box approach adopted by previous algorithms, we revisit and use the classical technique of output perturbation. While using output perturbation trivially addresses (2), it gives rise to challenges in addressing (1). We address this challenge by providing a novel analysis of the L2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We then integrate this algorithm, along with the state-of-the-art differentially private SGD, into Bismarck, an inRDBMS analytics system. Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms.
Developing Leadership Character in Business Programs
(2013)

Our objective is to encourage and enable leadership character development in business education. Building on a model of character strengths and their link to virtues, values, and ethical decision making, we describe an approach to develop leadership character at the individual, group, and organizational levels. We contrast this approach to existing practices that have focused on teaching functional content over character and address how business educators can enable leadership character development through their own behaviors, relationships, and structures. Most important, we provide concrete suggestions on how to integrate a focus on character development into existing business programs, both in terms of individual courses as well as the overall curriculum. We highlight that the development of leadership character must extend beyond student engagement in a course since “it takes a village” to develop character. ........................................................................................................................................................................
Approximating displacement with the body velocity integral
(2009)

In this paper, we present a technique for approximating the net displacement of a locomoting system over a gait without directly integrating its equations of motion. The approximation is based on a volume integral, which, among other benefits, is more open to optimization by algorithm or inspection than is the full displacement integral. Specifically, we develop the concept of a body velocity integral (BVI), which is computable over a gait as a volume integral via Stokes’s theorem. We then demonstrate that, given an appropriate choice of coordinates, the BVI for a gait approximates the displacement of the system over that gait. This consideration of coordinate choice is a new approach to locomotion problems, and provides significantly improved results over past attempts to apply Stokes’s theorem to gait analysis.
Octree-based decimation of marching cubes surfaces
(1996)

The marching cubes (MC) algorithm is a method for generating isosurfaces. It also generates an excessively large number of triangles to represent an isosurface; this increases the rendering time. This paper presents a decimation method to reduce the number of triangles generated. Decimation is carried out before creating a large number of triangles. Four major steps comprise the algorithm: surface tracking, merging, crack patching and triangulation. Surface tracking is an enhanced implementation of the MC algorithm. Starting from a seed point, the surface tracker visits only those cells likely to compose part of the desired isosurface. The cells making up the extracted surface are stored in an octree that is further processed. A bottom-up approach is taken in merging the cells containing a relatively flat approximating surface. The finer surface details are maintained. Cells are merged as long as the error due to such an operation is within a user-specified error parameter, or a cell acquires more than one connected surface component in it. A crack patching method is described that forces edges of smaller cells to lie along those of the larger neighboring cells. The overall saving in the number of triangles depends both on the specified error value and the nature of the data. Use of the hierarchical octree data structure also presents the potential of incremental representation of surfaces. We can generate a highly smoothed surface representation which can be progressively refined as the user-specified error value is decreased.
Talking to machines (statistically speaking)
(2002)

Statistical methods have long been the dominant approach in speech recognition and probabilistic modelling in ASR is now a mature technology. The use of statistical methods in other areas of spoken dialogue is however more recent and rather less mature. This paper reviews spoken dialogue systems from a statistical modelling perspective. The complete system is first presented as a partially observable Markov decision process. The various sub-components are then exposed by introducing appropriate intermediate variables. Samples of existing work are reviewed within this framework, including dialogue control and optimisation, semantic interpretation, goal detection, natural language generation and synthesis.
Mobile ECG measurement and analysis system using mobile phone as the base station
(2008)

In this paper, we introduce an ECG measurement, analysis and transmission system which uses a mobile phone as a base station. The system is based on a small-sized mobile ECG recording device which sends measurement data wirelessly to the mobile phone. In the mobile phone the received data is analyzed and if abnormalities are found part of the measurement data is sent to a server for the use of medical personnel. The prototype of the system was made with a portable ECG monitor and Nokia 6681 mobile phone. The results show that modern smart phones are indeed capable for this kind of tasks. Thus, with their good networking and data processing capabilities, they are a potential part of the future wireless health care systems.
STANDARDISATION AND CLASSIFICATION OF ALERTS GENERATED BY INTRUSION DETECTION SYSTEMS
(2016)

Intrusion detection systems are most popular de-fence mechanisms used to provide security to IT infrastructures. Organisation need best performance, so it uses multiple IDSs from different vendors. Different vendors are using different formats and protocols. Difficulty imposed by this is the generation of several false alarms. Major part of this work concentrates on the collection of alerts from different intrusion detection systems to represent them in IDMEF(Intrusion Detection Message Exchange Format) format. Alerts were collected from intrusion detection systems like snort, ossec, suricata etc. Later classification is attempted using machine learning technique, which helps to mitigate generation of false positives.